Audience The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector. Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences. Harleen Kaur, PhD, is doing project work with the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. Previously, she worked as a manager at DELBREC Industries, Pvt. Ltd., as well as an assistant professor of management at Asra Institute of Advanced Studies, Bhawanigarh, India.

• Preface xiii1 Factories of the Future 1Talwinder Singh and Davinder Singh1.0 Introduction 21.1 Factory of the Future 31.1.1 Plant Structure 31.1.2 Plant Digitization 41.1.3 Plant Processes 41.1.4 Industry of the Future: A Fully Integrated Industry 51.2 Current Manufacturing Environment 61.3 Driving Technologies and Market Readiness 81.4 Connected Factory, Smart Factory, and Smart Manufacturing 111.4.1 Potential Benefits of a Connected Factory 131.5 Digital and Virtual Factory 131.5.1 Digital Factory 131.5.2 Virtual Factory 141.6 Advanced Manufacturing Technologies 141.6.1 Advantages of Advanced Manufacturing Technologies 161.7 Role of Factories of the Future (FoF) in Manufacturing Performance 171.8 Socio-Econo-Techno Justification of Factories of the Future 17References 182 Industry 5.0 21Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh2.1 Introduction 222.1.1 Industry 5.0 for Manufacturing 222.1.1.1 Industrial Revolutions 232.1.2 Real Personalization in Industry 5.0 252.1.3 Industry 5.0 for Human Workers 282.2 Individualized Human-Machine-Interaction 292.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 312.4 Concerns in Industry 5.0 322.5 Humans Closer to the Design Process of Manufacturing 352.5.1 Enablers of Industry 5.0 362.6 Challenges and Enablers (Socio-Econo-Techno Justification) 372.6.1 Social Dimension 372.6.2 Governmental and Political Dimension 382.6.3 Interdisciplinarity 402.6.4 Economic Dimension 402.6.5 Scalability 412.7 Concluding Remarks 42References 433 Machine Learning – A Survey 47Navdeep Singh and Aanchal Goyal3.1 Introduction 483.2 Machine Learning 493.2.1 Unsupervised Machine Learning 503.2.2 Variety of Unsupervised Learning 513.2.3 Supervised Machine Learning 523.2.4 Categories of Supervised Learning 543.3 Reinforcement Machine Learning 543.3.1 Applications of Reinforcement Learning 563.3.2 Dimensionality Reduction 573.4 Importance of Dimensionality Reduction in Machine Learning 583.4.1 Methods of Dimensionality Reduction 583.4.1.1 Principal Component Analysis (PCA) 583.4.1.2 Linear Discriminant Analysis (LDA) 593.4.1.3 Generalized Discriminant Analysis (GDA) 613.5 Distance Measures 613.6 Clustering 653.6.1 Algorithms in Clustering 673.6.2 Applications of Clustering 683.6.3 Iterative Distance-Based Clustering 693.7 Hierarchical Model 703.8 Density-Based Clustering 723.8.1 Dbscan 723.8.2 Optics 733.9 Role of Machine Learning in Factories of the Future 743.10 Identification of the Probable Customers 753.11 Conclusion 78References 794 Understanding Neural Networks 83Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur4.1 Introduction 834.2 Components of Neural Networks 844.2.1 Neurons 854.2.2 Synapses and Weights 864.2.3 Bias 864.2.4 Architecture of Neural Networks 864.2.5 How Do Neural Networks Work? 874.2.6 Types of Neural Networks 884.2.6.1 Artificial Neural Network (ANN) 884.2.6.2 Recurrent Neural Network (RNN) 894.2.6.3 Convolutional Neural Network (CNN) 894.2.7 Learning Techniques in Neural Network 904.2.8 Applications of Neural Network 904.2.9 Advantages of Neural Networks 914.2.10 Disadvantages of Neural Network 914.2.11 Limitations of Neural Networks 924.3 Back-Propagation 924.3.1 Working of Back-Propagation 924.3.2 Types of Back-Propagation 934.3.2.1 Static Back-Propagation 934.3.2.2 Recurrent Back-Propagation 934.3.2.3 Advantages of Back-Propagation 944.3.2.4 Disadvantages of Back-Propagation 944.4 Activation Function (AF) 944.4.1 Sigmoid Active Function 944.4.1.1 Advantages 954.4.1.2 Disadvantages 954.4.2 RELU Activation Function 954.4.2.1 Advantages 964.4.2.2 Disadvantages 964.4.3 TANH Active Function 964.4.3.1 Advantages 974.4.3.2 Disadvantages 974.4.4 Linear Function 974.4.5 Advantages 984.4.6 Disadvantages 984.4.7 Softmax Function 984.4.8 Advantages 984.5 Comparison of Activation Functions 984.6 Machine Learning 994.6.1 Applications of Machine Learning 1004.7 Conclusion 100References 1015 Intelligent Machining 103Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak5.1 Introduction 1045.2 Requirements for the Developments of Intelligent Machining 1045.3 Components of Intelligent Machining 1055.3.1 Intelligent Sensors 1065.3.1.1 Features of Intelligent Sensors 1065.3.1.2 Functions of Intelligent Sensors 1075.3.1.3 Data Acquisition and Management System to Process and Store Signals 1115.3.2 Machine Learning and Knowledge Discovery Component 1135.3.3 Database Knowledge Discovery 1145.3.4 Programmable Logical Controller (PLC) 1155.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing 1175.3.6 Information Integration via Knowledge Graphs 1185.4 Conclusion 119References 1206 Advanced Maintenance and Reliability 121Davinder Singh and Talwinder Singh6.1 Introduction 1216.2 Condition-Based Maintenance 1226.3 Computerized Maintenance Management Systems (CMMS) 1246.4 Preventive Maintenance (PM) 1276.5 Predictive Maintenance (PdM) 1286.6 Reliability Centered Maintenance (RCM) 1296.6.1 RCM Principles 1306.7 Condition Monitoring and Residual Life Prediction 1316.8 Sustainability 1336.8.1 Role of Sustainability in Manufacturing 1346.9 Concluding Remarks 135References 1367 Digital Manufacturing 143Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak7.1 Introduction 1447.2 Product Life Cycle and Transition 1467.3 Digital Thread 1487.4 Digital Manufacturing Security 1507.5 Role of Digital Manufacturing in Future Factories 1517.6 Digital Manufacturing and CNC Machining 1527.6.1 Introduction to CNC Machining 1527.6.2 Equipment’s Used in CNC Machining 1537.6.3 Analyzing Digital Manufacturing Design Considerations 1537.6.4 Finishing of Part After Machining 1537.7 Additive Manufacturing 1547.7.1 Objective of Additive Manufacturing 1557.7.2 Design Consideration 1557.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries 1557.9 Conclusion 156References 1578 Artificial Intelligence in Machine Learning 161Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh8.1 Introduction 1628.2 Case Studies 1628.3 Advantages of A.I. in ml 1648.4 Artificial Intelligence – Basics 1668.4.1 History of A.I. 1668.4.2 Limitations of Human Mind 1668.4.3 Real Artificial Intelligence 1668.4.4 Artificial Intelligence Subfields 1678.4.5 The Positives of A.I. 1678.4.6 Machine Learning 1688.4.7 Machine Learning Models 1688.4.8 Neural Networks 1698.4.9 Constraints of Machine Learning 1708.4.10 Different Kinds of Machine Learning 1718.5 Application of Artificial Intelligence 1718.5.1 Expert Systems 1728.5.2 Natural Language Processing 1728.5.3 Speech Recognition 1728.5.4 Computer Vision 1728.5.5 Robotics 1728.6 Neural Networks (N.N.) Basics 1738.6.1 Application of Neural Networks 1738.6.2 Architecture of Neural Networks 1738.6.3 Working of Artificial Neural Networks 1758.7 Convolution Neural Networks 1768.7.1 Working of Convolutional Neural Networks 1768.7.2 Overview of CNN 1818.7.3 Working of CNN 1818.8 Image Classification 1828.8.1 Concept of Image Classification 1828.8.2 Type of Learning 1828.8.3 Features of Image Classification 1838.8.4 Examples of Image Classification 1838.9 Text Classification 1838.9.1 Text Classification Examples 1838.9.2 Phases of Text Classification 1848.9.3 Text Classification API 1868.10 Recurrent Neural Network 1868.10.1 Type of Recurrent Neural Network 1878.11 Building Recurrent Neural Network 1878.12 Long Short Term Memory Networks (LSTMs) 190References 1939 Internet of Things 195Davinder Singh9.1 Introduction 1959.2 M2M and Web of Things 1989.3 Wireless Networks 1999.4 Service Oriented Architecture 2039.5 Complexity of Networks 2059.6 Wireless Sensor Networks 2059.7 Cloud Computing 2079.8 Cloud Simulators 2119.9 Fog Computing 2149.10 Applications of IoT 2179.11 Research Gaps and Challenges in IoT 2209.12 Concluding Remarks 223References 22410 Product Life Cycle 229Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj10.1 Introduction 23010.2 Product Lifecycle Management (PLM) 23010.2.1 Why Product Lifecycle Management? 23110.2.2 Biological Product Lifecycle Stages 23110.2.3 An Example Related to Stages in Product Lifecycle Management 23310.2.4 Advanced Stages in Product Lifecycle Management 23410.2.5 Strategies of Product Lifecycle Management 23510.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies 23610.3.1 Considerations in High and Low-Level Pricing 23610.3.2 Penetration Pricing Strategy 23610.3.3 Example for Penetration Pricing Strategy 23710.3.4 Considerations in Penetration Pricing 23710.4 How Do Product Lifecycle Management Work? 24010.5 Application Process of Product Lifecycle Management (plm) 24110.6 Role of Unified Modelling Language (UML) 24210.6.1 UML Activity Diagrams 24310.7 Management of Product Information Throughout the Entire Product Lifecycle 24410.8 PDM System in an Organization 24510.8.1 Benefits of PDM 24510.8.2 How Does the PDM Work? 24510.8.3 The Services of Product Data Management 24610.9 System Architecture 24710.9.1 Process of System Architecture 24810.10 Concepts of Model-Based System Engineering (MBSE) 25010.10.1 Benefits of Model-Based System Engineering (mbse) 25110.11 Challenges of Post-COVID 19 in Manufacturing Sector 25110.12 Recent Updates in Product Life Cycle 25210.13 Conclusion 253References 25411 Case Studies 257Chandan Deep Singh and Harleen Kaur11.1 Case Study in a Two-Wheeler Manufacturing Industry 25811.1.1 Company Strategy 25811.1.2 Initiatives Towards Technological Advancement 26211.1.3 Management Initiatives 26311.1.4 Sustainable Development Goals 26511.1.5 Growth Framework with Customer Needs 26911.1.6 Vision for the Future 27011.2 Case Study in a Four-Wheeler Manufacturing Unit 27111.2.1 Company Principles 27111.2.2 Company Objectives 27111.2.3 Company Strategy and Business Initiatives 27211.2.4 Technology Initiatives 27211.2.5 Management Initiatives 27311.2.6 Quality 27511.2.7 Sustainable Development Goals 27611.2.8 Future Plan of Action 28011.3 Conclusions 28111.3.1 Limitations 28211.3.2 Suggestions for Future Work 282Index 285

Audience The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector. Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences. Harleen Kaur, PhD, is doing project work with the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. Previously, she worked as a manager at DELBREC Industries, Pvt. Ltd., as well as an assistant professor of management at Asra Institute of Advanced Studies, Bhawanigarh, India.

• Preface xiii1 Factories of the Future 1Talwinder Singh and Davinder Singh1.0 Introduction 21.1 Factory of the Future 31.1.1 Plant Structure 31.1.2 Plant Digitization 41.1.3 Plant Processes 41.1.4 Industry of the Future: A Fully Integrated Industry 51.2 Current Manufacturing Environment 61.3 Driving Technologies and Market Readiness 81.4 Connected Factory, Smart Factory, and Smart Manufacturing 111.4.1 Potential Benefits of a Connected Factory 131.5 Digital and Virtual Factory 131.5.1 Digital Factory 131.5.2 Virtual Factory 141.6 Advanced Manufacturing Technologies 141.6.1 Advantages of Advanced Manufacturing Technologies 161.7 Role of Factories of the Future (FoF) in Manufacturing Performance 171.8 Socio-Econo-Techno Justification of Factories of the Future 17References 182 Industry 5.0 21Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh2.1 Introduction 222.1.1 Industry 5.0 for Manufacturing 222.1.1.1 Industrial Revolutions 232.1.2 Real Personalization in Industry 5.0 252.1.3 Industry 5.0 for Human Workers 282.2 Individualized Human-Machine-Interaction 292.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 312.4 Concerns in Industry 5.0 322.5 Humans Closer to the Design Process of Manufacturing 352.5.1 Enablers of Industry 5.0 362.6 Challenges and Enablers (Socio-Econo-Techno Justification) 372.6.1 Social Dimension 372.6.2 Governmental and Political Dimension 382.6.3 Interdisciplinarity 402.6.4 Economic Dimension 402.6.5 Scalability 412.7 Concluding Remarks 42References 433 Machine Learning – A Survey 47Navdeep Singh and Aanchal Goyal3.1 Introduction 483.2 Machine Learning 493.2.1 Unsupervised Machine Learning 503.2.2 Variety of Unsupervised Learning 513.2.3 Supervised Machine Learning 523.2.4 Categories of Supervised Learning 543.3 Reinforcement Machine Learning 543.3.1 Applications of Reinforcement Learning 563.3.2 Dimensionality Reduction 573.4 Importance of Dimensionality Reduction in Machine Learning 583.4.1 Methods of Dimensionality Reduction 583.4.1.1 Principal Component Analysis (PCA) 583.4.1.2 Linear Discriminant Analysis (LDA) 593.4.1.3 Generalized Discriminant Analysis (GDA) 613.5 Distance Measures 613.6 Clustering 653.6.1 Algorithms in Clustering 673.6.2 Applications of Clustering 683.6.3 Iterative Distance-Based Clustering 693.7 Hierarchical Model 703.8 Density-Based Clustering 723.8.1 Dbscan 723.8.2 Optics 733.9 Role of Machine Learning in Factories of the Future 743.10 Identification of the Probable Customers 753.11 Conclusion 78References 794 Understanding Neural Networks 83Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur4.1 Introduction 834.2 Components of Neural Networks 844.2.1 Neurons 854.2.2 Synapses and Weights 864.2.3 Bias 864.2.4 Architecture of Neural Networks 864.2.5 How Do Neural Networks Work? 874.2.6 Types of Neural Networks 884.2.6.1 Artificial Neural Network (ANN) 884.2.6.2 Recurrent Neural Network (RNN) 894.2.6.3 Convolutional Neural Network (CNN) 894.2.7 Learning Techniques in Neural Network 904.2.8 Applications of Neural Network 904.2.9 Advantages of Neural Networks 914.2.10 Disadvantages of Neural Network 914.2.11 Limitations of Neural Networks 924.3 Back-Propagation 924.3.1 Working of Back-Propagation 924.3.2 Types of Back-Propagation 934.3.2.1 Static Back-Propagation 934.3.2.2 Recurrent Back-Propagation 934.3.2.3 Advantages of Back-Propagation 944.3.2.4 Disadvantages of Back-Propagation 944.4 Activation Function (AF) 944.4.1 Sigmoid Active Function 944.4.1.1 Advantages 954.4.1.2 Disadvantages 954.4.2 RELU Activation Function 954.4.2.1 Advantages 964.4.2.2 Disadvantages 964.4.3 TANH Active Function 964.4.3.1 Advantages 974.4.3.2 Disadvantages 974.4.4 Linear Function 974.4.5 Advantages 984.4.6 Disadvantages 984.4.7 Softmax Function 984.4.8 Advantages 984.5 Comparison of Activation Functions 984.6 Machine Learning 994.6.1 Applications of Machine Learning 1004.7 Conclusion 100References 1015 Intelligent Machining 103Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak5.1 Introduction 1045.2 Requirements for the Developments of Intelligent Machining 1045.3 Components of Intelligent Machining 1055.3.1 Intelligent Sensors 1065.3.1.1 Features of Intelligent Sensors 1065.3.1.2 Functions of Intelligent Sensors 1075.3.1.3 Data Acquisition and Management System to Process and Store Signals 1115.3.2 Machine Learning and Knowledge Discovery Component 1135.3.3 Database Knowledge Discovery 1145.3.4 Programmable Logical Controller (PLC) 1155.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing 1175.3.6 Information Integration via Knowledge Graphs 1185.4 Conclusion 119References 1206 Advanced Maintenance and Reliability 121Davinder Singh and Talwinder Singh6.1 Introduction 1216.2 Condition-Based Maintenance 1226.3 Computerized Maintenance Management Systems (CMMS) 1246.4 Preventive Maintenance (PM) 1276.5 Predictive Maintenance (PdM) 1286.6 Reliability Centered Maintenance (RCM) 1296.6.1 RCM Principles 1306.7 Condition Monitoring and Residual Life Prediction 1316.8 Sustainability 1336.8.1 Role of Sustainability in Manufacturing 1346.9 Concluding Remarks 135References 1367 Digital Manufacturing 143Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak7.1 Introduction 1447.2 Product Life Cycle and Transition 1467.3 Digital Thread 1487.4 Digital Manufacturing Security 1507.5 Role of Digital Manufacturing in Future Factories 1517.6 Digital Manufacturing and CNC Machining 1527.6.1 Introduction to CNC Machining 1527.6.2 Equipment’s Used in CNC Machining 1537.6.3 Analyzing Digital Manufacturing Design Considerations 1537.6.4 Finishing of Part After Machining 1537.7 Additive Manufacturing 1547.7.1 Objective of Additive Manufacturing 1557.7.2 Design Consideration 1557.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries 1557.9 Conclusion 156References 1578 Artificial Intelligence in Machine Learning 161Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh8.1 Introduction 1628.2 Case Studies 1628.3 Advantages of A.I. in ml 1648.4 Artificial Intelligence – Basics 1668.4.1 History of A.I. 1668.4.2 Limitations of Human Mind 1668.4.3 Real Artificial Intelligence 1668.4.4 Artificial Intelligence Subfields 1678.4.5 The Positives of A.I. 1678.4.6 Machine Learning 1688.4.7 Machine Learning Models 1688.4.8 Neural Networks 1698.4.9 Constraints of Machine Learning 1708.4.10 Different Kinds of Machine Learning 1718.5 Application of Artificial Intelligence 1718.5.1 Expert Systems 1728.5.2 Natural Language Processing 1728.5.3 Speech Recognition 1728.5.4 Computer Vision 1728.5.5 Robotics 1728.6 Neural Networks (N.N.) Basics 1738.6.1 Application of Neural Networks 1738.6.2 Architecture of Neural Networks 1738.6.3 Working of Artificial Neural Networks 1758.7 Convolution Neural Networks 1768.7.1 Working of Convolutional Neural Networks 1768.7.2 Overview of CNN 1818.7.3 Working of CNN 1818.8 Image Classification 1828.8.1 Concept of Image Classification 1828.8.2 Type of Learning 1828.8.3 Features of Image Classification 1838.8.4 Examples of Image Classification 1838.9 Text Classification 1838.9.1 Text Classification Examples 1838.9.2 Phases of Text Classification 1848.9.3 Text Classification API 1868.10 Recurrent Neural Network 1868.10.1 Type of Recurrent Neural Network 1878.11 Building Recurrent Neural Network 1878.12 Long Short Term Memory Networks (LSTMs) 190References 1939 Internet of Things 195Davinder Singh9.1 Introduction 1959.2 M2M and Web of Things 1989.3 Wireless Networks 1999.4 Service Oriented Architecture 2039.5 Complexity of Networks 2059.6 Wireless Sensor Networks 2059.7 Cloud Computing 2079.8 Cloud Simulators 2119.9 Fog Computing 2149.10 Applications of IoT 2179.11 Research Gaps and Challenges in IoT 2209.12 Concluding Remarks 223References 22410 Product Life Cycle 229Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj10.1 Introduction 23010.2 Product Lifecycle Management (PLM) 23010.2.1 Why Product Lifecycle Management? 23110.2.2 Biological Product Lifecycle Stages 23110.2.3 An Example Related to Stages in Product Lifecycle Management 23310.2.4 Advanced Stages in Product Lifecycle Management 23410.2.5 Strategies of Product Lifecycle Management 23510.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies 23610.3.1 Considerations in High and Low-Level Pricing 23610.3.2 Penetration Pricing Strategy 23610.3.3 Example for Penetration Pricing Strategy 23710.3.4 Considerations in Penetration Pricing 23710.4 How Do Product Lifecycle Management Work? 24010.5 Application Process of Product Lifecycle Management (plm) 24110.6 Role of Unified Modelling Language (UML) 24210.6.1 UML Activity Diagrams 24310.7 Management of Product Information Throughout the Entire Product Lifecycle 24410.8 PDM System in an Organization 24510.8.1 Benefits of PDM 24510.8.2 How Does the PDM Work? 24510.8.3 The Services of Product Data Management 24610.9 System Architecture 24710.9.1 Process of System Architecture 24810.10 Concepts of Model-Based System Engineering (MBSE) 25010.10.1 Benefits of Model-Based System Engineering (mbse) 25110.11 Challenges of Post-COVID 19 in Manufacturing Sector 25110.12 Recent Updates in Product Life Cycle 25210.13 Conclusion 253References 25411 Case Studies 257Chandan Deep Singh and Harleen Kaur11.1 Case Study in a Two-Wheeler Manufacturing Industry 25811.1.1 Company Strategy 25811.1.2 Initiatives Towards Technological Advancement 26211.1.3 Management Initiatives 26311.1.4 Sustainable Development Goals 26511.1.5 Growth Framework with Customer Needs 26911.1.6 Vision for the Future 27011.2 Case Study in a Four-Wheeler Manufacturing Unit 27111.2.1 Company Principles 27111.2.2 Company Objectives 27111.2.3 Company Strategy and Business Initiatives 27211.2.4 Technology Initiatives 27211.2.5 Management Initiatives 27311.2.6 Quality 27511.2.7 Sustainable Development Goals 27611.2.8 Future Plan of Action 28011.3 Conclusions 28111.3.1 Limitations 28211.3.2 Suggestions for Future Work 282Index 285

Audience The book will be read by academic researchers and industry engineers, managers, and specialists in industrial manufacturing and production, mechanical and electronics engineering and their allied disciplines. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies and advancements are continually made in this sector. Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. He has published over 100 papers in various peer-reviewed international journals and conferences. Harleen Kaur, PhD, is doing project work with the Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India. Previously, she worked as a manager at DELBREC Industries, Pvt. Ltd., as well as an assistant professor of management at Asra Institute of Advanced Studies, Bhawanigarh, India.

• Preface xiii1 Factories of the Future 1Talwinder Singh and Davinder Singh1.0 Introduction 21.1 Factory of the Future 31.1.1 Plant Structure 31.1.2 Plant Digitization 41.1.3 Plant Processes 41.1.4 Industry of the Future: A Fully Integrated Industry 51.2 Current Manufacturing Environment 61.3 Driving Technologies and Market Readiness 81.4 Connected Factory, Smart Factory, and Smart Manufacturing 111.4.1 Potential Benefits of a Connected Factory 131.5 Digital and Virtual Factory 131.5.1 Digital Factory 131.5.2 Virtual Factory 141.6 Advanced Manufacturing Technologies 141.6.1 Advantages of Advanced Manufacturing Technologies 161.7 Role of Factories of the Future (FoF) in Manufacturing Performance 171.8 Socio-Econo-Techno Justification of Factories of the Future 17References 182 Industry 5.0 21Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh2.1 Introduction 222.1.1 Industry 5.0 for Manufacturing 222.1.1.1 Industrial Revolutions 232.1.2 Real Personalization in Industry 5.0 252.1.3 Industry 5.0 for Human Workers 282.2 Individualized Human-Machine-Interaction 292.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 312.4 Concerns in Industry 5.0 322.5 Humans Closer to the Design Process of Manufacturing 352.5.1 Enablers of Industry 5.0 362.6 Challenges and Enablers (Socio-Econo-Techno Justification) 372.6.1 Social Dimension 372.6.2 Governmental and Political Dimension 382.6.3 Interdisciplinarity 402.6.4 Economic Dimension 402.6.5 Scalability 412.7 Concluding Remarks 42References 433 Machine Learning – A Survey 47Navdeep Singh and Aanchal Goyal3.1 Introduction 483.2 Machine Learning 493.2.1 Unsupervised Machine Learning 503.2.2 Variety of Unsupervised Learning 513.2.3 Supervised Machine Learning 523.2.4 Categories of Supervised Learning 543.3 Reinforcement Machine Learning 543.3.1 Applications of Reinforcement Learning 563.3.2 Dimensionality Reduction 573.4 Importance of Dimensionality Reduction in Machine Learning 583.4.1 Methods of Dimensionality Reduction 583.4.1.1 Principal Component Analysis (PCA) 583.4.1.2 Linear Discriminant Analysis (LDA) 593.4.1.3 Generalized Discriminant Analysis (GDA) 613.5 Distance Measures 613.6 Clustering 653.6.1 Algorithms in Clustering 673.6.2 Applications of Clustering 683.6.3 Iterative Distance-Based Clustering 693.7 Hierarchical Model 703.8 Density-Based Clustering 723.8.1 Dbscan 723.8.2 Optics 733.9 Role of Machine Learning in Factories of the Future 743.10 Identification of the Probable Customers 753.11 Conclusion 78References 794 Understanding Neural Networks 83Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur4.1 Introduction 834.2 Components of Neural Networks 844.2.1 Neurons 854.2.2 Synapses and Weights 864.2.3 Bias 864.2.4 Architecture of Neural Networks 864.2.5 How Do Neural Networks Work? 874.2.6 Types of Neural Networks 884.2.6.1 Artificial Neural Network (ANN) 884.2.6.2 Recurrent Neural Network (RNN) 894.2.6.3 Convolutional Neural Network (CNN) 894.2.7 Learning Techniques in Neural Network 904.2.8 Applications of Neural Network 904.2.9 Advantages of Neural Networks 914.2.10 Disadvantages of Neural Network 914.2.11 Limitations of Neural Networks 924.3 Back-Propagation 924.3.1 Working of Back-Propagation 924.3.2 Types of Back-Propagation 934.3.2.1 Static Back-Propagation 934.3.2.2 Recurrent Back-Propagation 934.3.2.3 Advantages of Back-Propagation 944.3.2.4 Disadvantages of Back-Propagation 944.4 Activation Function (AF) 944.4.1 Sigmoid Active Function 944.4.1.1 Advantages 954.4.1.2 Disadvantages 954.4.2 RELU Activation Function 954.4.2.1 Advantages 964.4.2.2 Disadvantages 964.4.3 TANH Active Function 964.4.3.1 Advantages 974.4.3.2 Disadvantages 974.4.4 Linear Function 974.4.5 Advantages 984.4.6 Disadvantages 984.4.7 Softmax Function 984.4.8 Advantages 984.5 Comparison of Activation Functions 984.6 Machine Learning 994.6.1 Applications of Machine Learning 1004.7 Conclusion 100References 1015 Intelligent Machining 103Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak5.1 Introduction 1045.2 Requirements for the Developments of Intelligent Machining 1045.3 Components of Intelligent Machining 1055.3.1 Intelligent Sensors 1065.3.1.1 Features of Intelligent Sensors 1065.3.1.2 Functions of Intelligent Sensors 1075.3.1.3 Data Acquisition and Management System to Process and Store Signals 1115.3.2 Machine Learning and Knowledge Discovery Component 1135.3.3 Database Knowledge Discovery 1145.3.4 Programmable Logical Controller (PLC) 1155.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing 1175.3.6 Information Integration via Knowledge Graphs 1185.4 Conclusion 119References 1206 Advanced Maintenance and Reliability 121Davinder Singh and Talwinder Singh6.1 Introduction 1216.2 Condition-Based Maintenance 1226.3 Computerized Maintenance Management Systems (CMMS) 1246.4 Preventive Maintenance (PM) 1276.5 Predictive Maintenance (PdM) 1286.6 Reliability Centered Maintenance (RCM) 1296.6.1 RCM Principles 1306.7 Condition Monitoring and Residual Life Prediction 1316.8 Sustainability 1336.8.1 Role of Sustainability in Manufacturing 1346.9 Concluding Remarks 135References 1367 Digital Manufacturing 143Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak7.1 Introduction 1447.2 Product Life Cycle and Transition 1467.3 Digital Thread 1487.4 Digital Manufacturing Security 1507.5 Role of Digital Manufacturing in Future Factories 1517.6 Digital Manufacturing and CNC Machining 1527.6.1 Introduction to CNC Machining 1527.6.2 Equipment’s Used in CNC Machining 1537.6.3 Analyzing Digital Manufacturing Design Considerations 1537.6.4 Finishing of Part After Machining 1537.7 Additive Manufacturing 1547.7.1 Objective of Additive Manufacturing 1557.7.2 Design Consideration 1557.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries 1557.9 Conclusion 156References 1578 Artificial Intelligence in Machine Learning 161Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh8.1 Introduction 1628.2 Case Studies 1628.3 Advantages of A.I. in ml 1648.4 Artificial Intelligence – Basics 1668.4.1 History of A.I. 1668.4.2 Limitations of Human Mind 1668.4.3 Real Artificial Intelligence 1668.4.4 Artificial Intelligence Subfields 1678.4.5 The Positives of A.I. 1678.4.6 Machine Learning 1688.4.7 Machine Learning Models 1688.4.8 Neural Networks 1698.4.9 Constraints of Machine Learning 1708.4.10 Different Kinds of Machine Learning 1718.5 Application of Artificial Intelligence 1718.5.1 Expert Systems 1728.5.2 Natural Language Processing 1728.5.3 Speech Recognition 1728.5.4 Computer Vision 1728.5.5 Robotics 1728.6 Neural Networks (N.N.) Basics 1738.6.1 Application of Neural Networks 1738.6.2 Architecture of Neural Networks 1738.6.3 Working of Artificial Neural Networks 1758.7 Convolution Neural Networks 1768.7.1 Working of Convolutional Neural Networks 1768.7.2 Overview of CNN 1818.7.3 Working of CNN 1818.8 Image Classification 1828.8.1 Concept of Image Classification 1828.8.2 Type of Learning 1828.8.3 Features of Image Classification 1838.8.4 Examples of Image Classification 1838.9 Text Classification 1838.9.1 Text Classification Examples 1838.9.2 Phases of Text Classification 1848.9.3 Text Classification API 1868.10 Recurrent Neural Network 1868.10.1 Type of Recurrent Neural Network 1878.11 Building Recurrent Neural Network 1878.12 Long Short Term Memory Networks (LSTMs) 190References 1939 Internet of Things 195Davinder Singh9.1 Introduction 1959.2 M2M and Web of Things 1989.3 Wireless Networks 1999.4 Service Oriented Architecture 2039.5 Complexity of Networks 2059.6 Wireless Sensor Networks 2059.7 Cloud Computing 2079.8 Cloud Simulators 2119.9 Fog Computing 2149.10 Applications of IoT 2179.11 Research Gaps and Challenges in IoT 2209.12 Concluding Remarks 223References 22410 Product Life Cycle 229Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj10.1 Introduction 23010.2 Product Lifecycle Management (PLM) 23010.2.1 Why Product Lifecycle Management? 23110.2.2 Biological Product Lifecycle Stages 23110.2.3 An Example Related to Stages in Product Lifecycle Management 23310.2.4 Advanced Stages in Product Lifecycle Management 23410.2.5 Strategies of Product Lifecycle Management 23510.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies 23610.3.1 Considerations in High and Low-Level Pricing 23610.3.2 Penetration Pricing Strategy 23610.3.3 Example for Penetration Pricing Strategy 23710.3.4 Considerations in Penetration Pricing 23710.4 How Do Product Lifecycle Management Work? 24010.5 Application Process of Product Lifecycle Management (plm) 24110.6 Role of Unified Modelling Language (UML) 24210.6.1 UML Activity Diagrams 24310.7 Management of Product Information Throughout the Entire Product Lifecycle 24410.8 PDM System in an Organization 24510.8.1 Benefits of PDM 24510.8.2 How Does the PDM Work? 24510.8.3 The Services of Product Data Management 24610.9 System Architecture 24710.9.1 Process of System Architecture 24810.10 Concepts of Model-Based System Engineering (MBSE) 25010.10.1 Benefits of Model-Based System Engineering (mbse) 25110.11 Challenges of Post-COVID 19 in Manufacturing Sector 25110.12 Recent Updates in Product Life Cycle 25210.13 Conclusion 253References 25411 Case Studies 257Chandan Deep Singh and Harleen Kaur11.1 Case Study in a Two-Wheeler Manufacturing Industry 25811.1.1 Company Strategy 25811.1.2 Initiatives Towards Technological Advancement 26211.1.3 Management Initiatives 26311.1.4 Sustainable Development Goals 26511.1.5 Growth Framework with Customer Needs 26911.1.6 Vision for the Future 27011.2 Case Study in a Four-Wheeler Manufacturing Unit 27111.2.1 Company Principles 27111.2.2 Company Objectives 27111.2.3 Company Strategy and Business Initiatives 27211.2.4 Technology Initiatives 27211.2.5 Management Initiatives 27311.2.6 Quality 27511.2.7 Sustainable Development Goals 27611.2.8 Future Plan of Action 28011.3 Conclusions 28111.3.1 Limitations 28211.3.2 Suggestions for Future Work 282Index 285